Dinggang Shen
Research Area: Intelligent Medicine
Office: Room 432, BME Building
Brief CV
Research Interests
Services to External Academic Communities
Lab Introduction

Brief CV:

Professor Dinggang Shen (IEEE Fellow, AIMBE Fellow, IAPR Fellow and MICCAI Fellow) is the Founding Dean of the School of Biomedical Engineering at ShanghaiTech University. Before joining ShanghaiTech, he was a tenured Professor of RadiologyBiomedical Research Imaging Center (BRIC)Computer Science, and Biomedical Engineering at the University of North Carolina at Chapel Hill (UNC), USA. He was also the Director of faculty development in the Department of Radiology, Director of Imaging Information Center, Director of IDEA lab, and Director of Image Analysis Core of BRIC at UNC. He is the Co-CEO of Shanghai United Imaging Intelligence Co., Ltd. He has published more than 1,590 papers, with an H-index of 128 and more than 70,000 citations. He serves in editorial boards of eight international journals, and also served as the General Chair of MICCAI 2019.

Professor Shen has been involved in the application of machine learning and artificial intelligence in medical image computing for a long time, including early brain development, early diagnosis, and the prediction of Alzheimer's disease, as well as diagnosis, prognosis and radiotherapy of brain tumor, breast cancer and prostate cancer. He is a pioneering scientist carrying out imaging AI research all over the world and is one of the first to apply deep learning to medical imaging (2012).

Research Interests:

  • Medical Image Analysis

  • Pattern Recognition

  • Computer Vision


Services to External Academic Communities:

Current Editorship:

Past Editorship:


Lab Introduction:

IDEA Lab at School of BME in ShanghaiTech University is exploring the following directions of research:

  • Neuroimaging and neuroscience

    Developing novel image analysis tools, including image registration, segmentation, and classification, for quantification of brain imaging data with applications to early brain development, aging, and disorders.

  • Cancer-related research

    Doing multi-modal image-based diagnosis, image-guided treatment, and therapy assessment on breast cancer, prostate cancer, brain tumor and other cancers.

  • Next-generation image acquisition

    Investigating novel AI-enabled computational methods for next-generation imaging scanners, including fast MR imaging, low-dose CT imaging, low-dose PET imaging, and image quality enhancement.